Abstract
An important recent literature discusses the use of Bayesian methods using numerical approximations which are likelihood free (Approximate Bayesian Computation - ABC). These methods allow inferences on issueshithertountractable due to the complexity of the evaluation of the likelihood function. We discuss how to formulate ABC methods using Generalized Empirical Likelihood, extending the work of Mergensen et al
(2012). These methods allow a formulation computationally more efficient than the usual ABC methods, and can be used even when it is not possible to calculate the moment conditions analytically through the use ofsimulated moment conditions. We apply these methods in the estimation of Stochastic Volatility Models.